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AIOps: A Beginners Guide - Part 1

Vinay Chandrasekhar
Elastic

Artificial Intelligence for IT Operations (or AIOps for short) continues to be a hot topic among developers, SREs, or DevOps professionals. The case for AIOps is especially crucial given the expansive nature of today's observability efforts across hybrid and multi-cloud environments. As with most observability challenges, AIOps starts with telemetry data: metrics, logs, traces, and events.

Once IT operations teams collect and begin to analyze the data, the benefit of AIOps becomes rapidly clear. AIOps aims to accurately and proactively identify areas that need attention and assist IT teams in solving issues faster. As human beings, we cannot keep up with analyzing petabytes of raw observability data. Adding AIOps delivers a layer of intelligence via analytics and automation to help reduce overhead for a team. Let's dive in to answer common questions on this critical topic.


What Is AIOps and How Can It Help Me?

Simply put, AIOps is the ability of software systems to ease and assist IT operations via the use of AI/ML and related analytical technologies. AIOps capabilities can be applied to various operational data, including ingestion and processing of log data, traces, metrics, and much more.

Seeking to clarify the often murky and confusing world of AIOps are Gartner, Forrester and others who provide market definition. AIOps can help significantly reduce the time and effort to detect, understand, investigate, determine root cause, and remediate issues and incidents faster. Saving time during troubleshooting can, in turn, help IT personnel focus more of their energy on higher-value tasks and projects.

Why Do You Need AIOps as Part of Your Observability Strategy?

Many recent articles (Gartner glossary for AIOps, Forrester AIOps reports) describe the dynamics in the IT market. From digital transformation initiatives to cloud migration to distributed, hybrid, or cloud-native application deployments, these dynamics are dramatically changing the IT operations landscape.

The landscape changes have the following three characteristics:

Data volume: The volume of data for observability continues to increase exponentially.

Complexity: Applications, workloads, and deployments continue to become more complex, ephemeral, and distributed.

Pace of change: The rate at which changes (application and infrastructure) occur is faster than ever before.

These are not mutually exclusive. In some ways, quite the opposite. For example, high rates of change and complex deployments utilizing auto-scaling mean an even higher volume of data. This increasing complexity means that humans will depend on systems and automation to keep up with the changes. For this reason, AIOps will play a key role in responding to these operational and business challenges.

Leveraging AI/ML to roll up data, summarize it, and intelligently tier the data for storage can help alleviate some of the volume challenges. Explicit visual depictions of an application environment (infrastructure and service dependency maps) and contextual navigation help align troubleshooting efforts with how users think of their deployment. Furthermore, auto-surfacing of problems and root causes analyses will address some of the other complex challenges.

Observability products will need to keep track of all application and infrastructure changes and correlate those changes with system behavior and user experience because change is often the root cause of acute, anomalous behavior. An upgrade or patch for a new feature with unintended consequences is a typical example. Enabling those correlations helps teams be more agile and adept at keeping pace with those frequent changes helping sustain service performance.

AIOps play a key role and can help navigate these challenges effectively, freeing up operations teams to focus on more important work when properly implemented and used.

Which Observability Use Cases Are Best for AIOps?

Several observability workflows and use cases are already very well served with the application of AIOps techniques and technologies, for example:

■ Service degradation such as sudden or unexpected variations in latency can be detected via anomaly detection.

■ Massive volumes of data, such as unstructured or semi-structured log messages, can be automatically classified, categorized, and summarized to help ease consumption and analysis.

■ Multiple symptoms, events, and issues can be correlated to help cut down alert "noise" and reduce time to root cause determination.

■ Automatic health scoring based on an assessment of impact, the extent of anomalies, and other measures help surface the most critical issues first, further reducing noise.

In the more well-understood and time-tested "if this is the symptom, then this is the likely root cause" relationships, AIOps can help automatically look for, detect, and classify those symptoms and surface those potential root causes. Ultimately, AIOps can enable remediation actions to fix routine or trivial issues and reduce burnout for operations teams

In a future blog, we will dive deeper into key use cases and how you can identify scenarios to apply AIOps in day-to-day operations.

Vinay Chandrasekhar is Sr. Principal Product Manager, Observability, at Elastic

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AIOps: A Beginners Guide - Part 1

Vinay Chandrasekhar
Elastic

Artificial Intelligence for IT Operations (or AIOps for short) continues to be a hot topic among developers, SREs, or DevOps professionals. The case for AIOps is especially crucial given the expansive nature of today's observability efforts across hybrid and multi-cloud environments. As with most observability challenges, AIOps starts with telemetry data: metrics, logs, traces, and events.

Once IT operations teams collect and begin to analyze the data, the benefit of AIOps becomes rapidly clear. AIOps aims to accurately and proactively identify areas that need attention and assist IT teams in solving issues faster. As human beings, we cannot keep up with analyzing petabytes of raw observability data. Adding AIOps delivers a layer of intelligence via analytics and automation to help reduce overhead for a team. Let's dive in to answer common questions on this critical topic.


What Is AIOps and How Can It Help Me?

Simply put, AIOps is the ability of software systems to ease and assist IT operations via the use of AI/ML and related analytical technologies. AIOps capabilities can be applied to various operational data, including ingestion and processing of log data, traces, metrics, and much more.

Seeking to clarify the often murky and confusing world of AIOps are Gartner, Forrester and others who provide market definition. AIOps can help significantly reduce the time and effort to detect, understand, investigate, determine root cause, and remediate issues and incidents faster. Saving time during troubleshooting can, in turn, help IT personnel focus more of their energy on higher-value tasks and projects.

Why Do You Need AIOps as Part of Your Observability Strategy?

Many recent articles (Gartner glossary for AIOps, Forrester AIOps reports) describe the dynamics in the IT market. From digital transformation initiatives to cloud migration to distributed, hybrid, or cloud-native application deployments, these dynamics are dramatically changing the IT operations landscape.

The landscape changes have the following three characteristics:

Data volume: The volume of data for observability continues to increase exponentially.

Complexity: Applications, workloads, and deployments continue to become more complex, ephemeral, and distributed.

Pace of change: The rate at which changes (application and infrastructure) occur is faster than ever before.

These are not mutually exclusive. In some ways, quite the opposite. For example, high rates of change and complex deployments utilizing auto-scaling mean an even higher volume of data. This increasing complexity means that humans will depend on systems and automation to keep up with the changes. For this reason, AIOps will play a key role in responding to these operational and business challenges.

Leveraging AI/ML to roll up data, summarize it, and intelligently tier the data for storage can help alleviate some of the volume challenges. Explicit visual depictions of an application environment (infrastructure and service dependency maps) and contextual navigation help align troubleshooting efforts with how users think of their deployment. Furthermore, auto-surfacing of problems and root causes analyses will address some of the other complex challenges.

Observability products will need to keep track of all application and infrastructure changes and correlate those changes with system behavior and user experience because change is often the root cause of acute, anomalous behavior. An upgrade or patch for a new feature with unintended consequences is a typical example. Enabling those correlations helps teams be more agile and adept at keeping pace with those frequent changes helping sustain service performance.

AIOps play a key role and can help navigate these challenges effectively, freeing up operations teams to focus on more important work when properly implemented and used.

Which Observability Use Cases Are Best for AIOps?

Several observability workflows and use cases are already very well served with the application of AIOps techniques and technologies, for example:

■ Service degradation such as sudden or unexpected variations in latency can be detected via anomaly detection.

■ Massive volumes of data, such as unstructured or semi-structured log messages, can be automatically classified, categorized, and summarized to help ease consumption and analysis.

■ Multiple symptoms, events, and issues can be correlated to help cut down alert "noise" and reduce time to root cause determination.

■ Automatic health scoring based on an assessment of impact, the extent of anomalies, and other measures help surface the most critical issues first, further reducing noise.

In the more well-understood and time-tested "if this is the symptom, then this is the likely root cause" relationships, AIOps can help automatically look for, detect, and classify those symptoms and surface those potential root causes. Ultimately, AIOps can enable remediation actions to fix routine or trivial issues and reduce burnout for operations teams

In a future blog, we will dive deeper into key use cases and how you can identify scenarios to apply AIOps in day-to-day operations.

Vinay Chandrasekhar is Sr. Principal Product Manager, Observability, at Elastic

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Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...